In [ ]:
from PIL import Image, ImageEnhance
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import pandas as pd
import math
import requests
import json
import re
import csv
directory_path = os.getcwd()
parent_directory_path = os.path.dirname(directory_path)
csv_path = os.path.join(parent_directory_path, 'Model\\condo_data_new_FINAL_test.csv')
gt_masked_image_path = os.path.join(parent_directory_path, 'Model\\buildings\\test')
generated_image_path = os.path.join(parent_directory_path, 'Model\\buildings\\final_buildings_output_2')
# Read the CSV file
data = pd.read_csv(csv_path)
# Function to extract the numeric part of the filename
def extract_numeric_part(filename):
numeric_part = ''.join(filter(str.isdigit, filename))
return int(numeric_part) if numeric_part else None
def create_binary_mask(arr, target_color, threshold=30):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
def extract_building_regions(arr, target_color, threshold=10):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
# def find_max_building_storeys(gpr):
# max_building_storeys= 0
# if gpr >= 0 and gpr < 1.4:
# max_building_storeys = 5
# elif gpr >= 1.4 and gpr < 1.6:
# max_building_storeys = 12
# elif gpr >= 1.6 and gpr < 2.1:
# max_building_storeys = 24
# elif gpr >= 2.1 and gpr < 2.8:
# max_building_storeys = 36
# elif gpr >= 2.8:
# max_building_storeys = 48 ## by right got no limit
# return max_building_storeys
def masked_rgb(simp_gpr):
rgb = [0,0,0]
if simp_gpr == 1.4:
rgb = [255, 10, 169]
elif simp_gpr == 1.6:
rgb = [200,130,60]
elif simp_gpr == 2.1:
rgb = [0,0,255]
elif simp_gpr == 2.8:
rgb = [255,0,0]
elif simp_gpr == 3.0:
rgb =[0,0,0]
return rgb
'''
pink, [255, 10, 169]
brown, [200,130,60]
cyan, [0,255,255]
red, [255,0,0]
black, [0,0,0]
green, [0,255,0]
blue, [0,0,255]
yellow, [255, 255, 0]
'''
# absolute_accuracies = []
# losses =[]
# images =[]
# sanity_ratios =[]
gprs =[]
generated_gprs =[]
sanity_ratios =[]
# Iterate through the images in the generated_image_path
for image_file in os.listdir(generated_image_path):
if image_file.endswith('.png'):
image_index = extract_numeric_part(image_file)
# Construct the path for the corresponding masked image
gt_mask_image_filename = f"{image_index}.png"
gt_mask_image = os.path.join(gt_masked_image_path, gt_mask_image_filename)
open_gt_mask_image = Image.open(gt_mask_image)
mask_crop_box = (512, 0, 1024, 512) # right side
mask_image = open_gt_mask_image.crop(mask_crop_box) #gt_mask is concatenated gt and mask
gt_crop_box = (0, 0, 512, 512) # left side
gt_image = open_gt_mask_image.crop(gt_crop_box)
generated_image = os.path.join(generated_image_path, image_file)
generated_image = Image.open(generated_image)
# Check if the image index matches any index in the CSV
matched_row = data[data['key1'] == image_index]
if not matched_row.empty:
# Extract the GPR value for the matched row
gpr_value = matched_row['GPR'].iloc[0]
storey = matched_row['storeys'].iloc[0]
simplified_gpr_value = matched_row['simp_gpr'].iloc[0]
actual_site_area = matched_row['area'].iloc[0]
actual_site_area = actual_site_area.replace(',', '')
actual_site_area = float(actual_site_area[:-4])
gpr_value = float(gpr_value)
storey = int(storey)
mask_array = np.array(mask_image)
generated_array = np.array(generated_image)
mask_color = [0,255,0] # green
site_mask = create_binary_mask(mask_array, mask_color)
site_area_array = generated_array.copy()
site_area_array[~site_mask] = [255, 255, 255, 255] # making non-masked region white RMB ITS 4 CHANNELS NOW
site_area_image = Image.fromarray(site_area_array)
mask_color = masked_rgb(simplified_gpr_value)
building_mask = extract_building_regions(site_area_array, mask_color)
buildings_image = Image.fromarray(building_mask)
plt.figure(figsize=(20, 5))
plt.subplot(1, 4, 1)
plt.imshow(mask_image)
plt.title('Mask Image')
plt.axis('off')
plt.subplot(1, 4, 2)
plt.imshow(gt_image)
plt.title('GT Image')
plt.axis('off')
plt.subplot(1, 4, 3)
plt.imshow(generated_image)
plt.title('Generated Image')
plt.axis('off')
plt.subplot(1, 4, 4)
plt.imshow(buildings_image, cmap='gray')
plt.title('Buildings Image')
plt.axis('off')
plt.show()
building_pixels = np.sum(building_mask)
mask_pixels = np.sum(site_mask)
msq_per_pixel = actual_site_area/mask_pixels
building_area = msq_per_pixel * building_pixels
#max_storeys = find_max_building_storeys(gpr_value)
generated_gpr = building_area*storey/actual_site_area
gprs.append(gpr_value)
generated_gprs.append(generated_gpr)
print(f'Image: {image_file}, GPR: {gpr_value}, Simplified GPR: {simplified_gpr_value}, Storeys:{storey}, Site area: {actual_site_area}, Building pixels: {building_pixels}, Mask pixels: {mask_pixels}, Generated GPR: {generated_gpr}')
#sanity check. ratios should be about 0.75
ratio = mask_pixels/actual_site_area
sanity_ratios.append(ratio)
total_data = len(gprs)
accuracies = []
absolute_error =[]
square_error =[]
for tar_gpr, gen_gpr in zip(gprs, generated_gprs):
accuracies.append(abs((tar_gpr-gen_gpr)/tar_gpr))
absolute_error.append(abs(tar_gpr-gen_gpr))
square_error.append((tar_gpr-gen_gpr)**2)
accuracy = sum(accuracies)/total_data
mean_abs_error = sum(absolute_error)/total_data
root_squared_error = math.sqrt(sum(square_error)/total_data)
print(f"Accuracies:{accuracies} \nSquare error:{square_error} \nAbsolute error:{absolute_error} ")
print(f"\nAccuracy:{accuracy} MAE:{mean_abs_error} RMSE:{root_squared_error}")
Image: 1040.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 23065.1, Building pixels: 1018, Mask pixels: 16203, Generated GPR: 0.31413935690921435
Image: 1074.png, GPR: 2.5, Simplified GPR: 2.8, Storeys:12, Site area: 37265.0, Building pixels: 3101, Mask pixels: 27439, Generated GPR: 1.3561718721527753
Image: 1076.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:36, Site area: 10414.2, Building pixels: 819, Mask pixels: 8554, Generated GPR: 3.4468085106382977
Image: 1102.png, GPR: 1.6, Simplified GPR: 1.6, Storeys:12, Site area: 6157.3, Building pixels: 8, Mask pixels: 4778, Generated GPR: 0.020092088740058602
Image: 1180.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:15, Site area: 19547.0, Building pixels: 1776, Mask pixels: 14355, Generated GPR: 1.8557993730407525
Image: 1379.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 17455.9, Building pixels: 1912, Mask pixels: 12216, Generated GPR: 0.7825802226588081
Image: 145.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:15, Site area: 22094.4, Building pixels: 1630, Mask pixels: 16292, Generated GPR: 1.500736557819789
Image: 1484.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 10097.1, Building pixels: 1874, Mask pixels: 7670, Generated GPR: 4.1535853976531945
Image: 1602.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13564.8, Building pixels: 2080, Mask pixels: 9962, Generated GPR: 3.5494880546075085
Image: 1655.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:18, Site area: 27418.2, Building pixels: 2663, Mask pixels: 21829, Generated GPR: 2.195886206422649
Image: 1670.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:13, Site area: 17940.2, Building pixels: 1729, Mask pixels: 11806, Generated GPR: 1.9038624428256818
Image: 1796.png, GPR: 2.8, Simplified GPR: 2.8, Storeys:17, Site area: 13877.2, Building pixels: 1092, Mask pixels: 9365, Generated GPR: 1.982274426054458
Image: 1811.png, GPR: 1.4, Simplified GPR: 1.4, Storeys:5, Site area: 7255.7, Building pixels: 632, Mask pixels: 5237, Generated GPR: 0.6033988924957037
Image: 1876.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:19, Site area: 10502.8, Building pixels: 1728, Mask pixels: 8276, Generated GPR: 3.967133881101982
Image: 191.png, GPR: 3.5, Simplified GPR: 3.0, Storeys:18, Site area: 13000.3, Building pixels: 2973, Mask pixels: 9208, Generated GPR: 5.8116854908774975
Image: 2000.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:17, Site area: 13241.8, Building pixels: 2258, Mask pixels: 9680, Generated GPR: 3.965495867768595
Image: 434.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:16, Site area: 39401.6, Building pixels: 3152, Mask pixels: 28711, Generated GPR: 1.7565393054926683
Image: 489.png, GPR: 2.1, Simplified GPR: 2.1, Storeys:15, Site area: 28692.65, Building pixels: 2736, Mask pixels: 20526, Generated GPR: 1.9994153756211632
Image: 491.png, GPR: 3.0, Simplified GPR: 3.0, Storeys:16, Site area: 18747.8, Building pixels: 1357, Mask pixels: 13058, Generated GPR: 1.6627354878235565
Image: 568.png, GPR: 3.4, Simplified GPR: 3.0, Storeys:19, Site area: 14344.0, Building pixels: 1532, Mask pixels: 10510, Generated GPR: 2.7695528068506183 Accuracies:[0.7756147450648468, 0.45753125113888987, 0.23100303951367784, 0.9874424445374633, 0.38140020898641586, 0.4410141266722799, 0.4640226579215039, 0.38452846588439815, 0.1831626848691695, 0.04566009829649947, 0.32004912756225645, 0.2920448478376935, 0.5690007910744973, 0.8891113719533247, 0.6604815688221422, 0.321831955922865, 0.16355271167015797, 0.0478974401803985, 0.44575483739214783, 0.18542564504393577] Square error:[1.1790933362135343, 1.308342786054487, 0.41836124943413316, 2.496109008061751, 1.3091950747339351, 0.38120718145204685, 1.6880854921859698, 1.3307592696786787, 0.3019371221563442, 0.009194164582126834, 0.803062521378354, 0.6686751142845657, 0.6345733244770714, 3.48618892995895, 5.343889808733537, 0.9321822706782322, 0.11796524867145866, 0.010117266661431703, 1.7882763755265014, 0.39746366334993366] Absolute error:[1.0858606430907856, 1.1438281278472247, 0.6468085106382979, 1.5799079112599415, 1.1442006269592475, 0.6174197773411918, 1.2992634421802107, 1.1535853976531945, 0.5494880546075085, 0.09588620642264889, 0.896137557174318, 0.8177255739455418, 0.7966011075042962, 1.867133881101982, 2.3116854908774975, 0.965495867768595, 0.34346069450733174, 0.10058462437883686, 1.3372645121764435, 0.6304471931493816] Accuracy:0.4123265010172282 MAE:0.9691392600292238 RMSE:1.1091591231260067